Optical spectroscopy is commonly used to determine the concentration of chemical species in gaseous, liquid, and solid samples. The amount of light absorbed by a particular chemical species in a sample is often linearly related to its concentration through Beer's Law, A=εlc, where A is termed absorbance, ε is a constant specific to the chemical species, l is the path length of light, and c is the concentration of the species. A=log(I0/I), where I0 is the intensity of light incident on a sample containing the chemical to be measured, and I is the intensity of light after it has passed through the sample.
For nontransparent materials, including complex materials such as liquids, powders, tablets, natural materials (e.g., soil and agricultural products), blood, skin, and muscle, optical information can be collected via diffuse reflectance spectroscopy. In this setting, A=log(I100/IR), where I100 is the amount of light reflected from a 100% reflecting diffuse reflectance standard, the equivalent of the incident light, and IR is the amount of light reflected from the solution under study. The concentration of a chemical component in one of these complex materials is related to A, though often not linearly. Sophisticated mathematical techniques, such as, for example, partial least squares (“PLS”) regression and other multivariate calibration methods can be used to determine a relationship between the concentration of a chemical species and absorbance by the sample. Once these calibration models are derived, they can be used to determine the chemical composition of a sample by measuring absorbance of the sample in the transmittance or reflectance mode.
In the laboratory, it is relatively straightforward to measure the absorbance A. One method uses a temperature controlled dual beam spectrograph. The sample solution is placed in one chamber, and the amount of light transmitted through the sample is measured. The solvent alone is placed in the second chamber where the incident light is measured. The ratio is determined electronically and the absorbance reported.
Laboratory spectrographs are generally large, expensive, and not portable. Recently, smaller spectrographs have been introduced that allow absorbance measurements to be performed in the “field,” meaning that the spectrometer equipment can be brought to the sample, rather than requiring that the sample be brought to the lab.
Spectroscopic measurements are now commonly made of agricultural products, in the ocean, in forests, on manufacturing production lines, and on the human body. Most of these measurements are made in the reflectance mode in which a fiber optic based sensor directs light to the sample and measures light reflected back from the sample. Field measurements are often made in an ongoing, continuous manner, to observe temporal changes of a quantity measured spectroscopically. Sometimes these measurements are made in a “hostile” environment, where it is difficult to make electrical measurements because electronic instruments experience interference (e.g., in an MRI machine) or degradation (e.g., due to a smoke stack or waste water).
Usually spectroscopic measurements are made by first collecting light from the 100% reflectance standard, storing that number, then attaching the sensor to the sample and collecting a series of spectra. The initial reference measurement of the 100% reflectance standard is used to calibrate absorbance from all subsequent sample spectra. This process, unfortunately, can introduce significant error, especially when the target absorbance changes are small and present in a complex, interfering chemical mixture, such as those studied in the field. Over time there can be changes in the lamp output and the detector sensitivity, which alter the intensity and spectral temperature of light impinging on the sample. If these changes are not detected and corrected in real time in the absorbance calculation, the measured value of A can be erroneous, and an accurate concentration of the measured quantity may not be made.
In addition, because different spectrographs can respond differently to the same sample, spectrographs may need to be standardized so that the different spectrographs provide consistent information. Because the cost of developing clinical parameter models based on partial least squares (“PLS”) regression can be prohibitive, the practical application of PLS regression models to quantitative tissue spectroscopy depends largely on the feasibility of applying PLS models developed on a primary (“master”) instrument to tissue measurements acquired with other (“slave”) instruments. From a measurement standpoint, a non-invasive sensor that utilizes PLS-modeled absorbance spectra to predict clinical parameters of interest is a relatively simple instrument, consisting primarily of a light source, a wavelength dispersive device combined with a detector (spectrometer), and a fiber-optic tissue sampling probe. From a manufacturing standpoint, however, it is difficult to exactly calibrate the performance of these components in individual sensor units. Consequently, the optical spectrum of a single tissue sample that is measured with two different instruments can result in mismatched spectra reported by the two instruments.
If the two measured spectra were applied to a PLS model that was developed on one of the sensors, estimates of the modeled parameter will also be different. Approaches developed to solve this problem typically require the measurement of one or more samples (transfer samples) by both the master and slave instruments, with the purpose of calculating an “instrument correction factor,” or “transfer function” that can be used to match the spectral output of the slave device to that of the master device. These standardization approaches, however, require that the transfer samples be selected from the same population used in deriving the calibration model, which is not practical in medical applications where the “samples” are human tissue. While one might consider the use of tissue-mimicking phantom samples as viable transfer samples, batch-to-batch errors in phantom creation as well as the poor long-term stability of virtually all known tissue phantom formulations, can severely limit their use in this regard.